TY - JOUR
T1 - Onlineforecast: An R package for adaptive and recursive forecasting
AU - Bacher, Peder
AU - Bergsteinsson, Hjörleifur G.
AU - Frölke, Linde
AU - Sørensen, Mikkel L.
AU - Lemos-Vinasco, Julian
AU - Liisberg, Jon
AU - Møller, Jan Kloppenborg
AU - Nielsen, Henrik Aalborg
AU - Madsen, Henrik
PY - 2023
Y1 - 2023
N2 - Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the R package onlineforecast that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular applications and run models in an operational setting. The package also allows users to easily replace parts of the setup, e.g. using new methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied for online forecasting in all fields.
AB - Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the R package onlineforecast that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular applications and run models in an operational setting. The package also allows users to easily replace parts of the setup, e.g. using new methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied for online forecasting in all fields.
KW - Recursive estimation
KW - Adaptive
KW - Non-linear transformation
KW - Time series
KW - Energy
KW - Online Forecasting
KW - Prediction
KW - R
U2 - 10.32614/RJ-2023-031
DO - 10.32614/RJ-2023-031
M3 - Journal article
SN - 2073-4859
VL - 15
SP - 173
EP - 194
JO - R Journal
JF - R Journal
IS - 1
ER -